This disclosure relates generally to distribution of content items by an online system, and more specifically to controlling distribution of content items by predicting whether a content item violates policies of an online system using a machine learning based model.
Online systems, for example, social networking systems distribute content received from content providers to users of online systems. Online systems typically enforce certain content policies. An online system may withhold content items violating policies of the online system from users. As an example, of content policy, an online system may prohibit content items that use certain keywords, for example, swear words. As another example, an online system may prohibit certain types of images. An online system may withhold content items violating policies of the online system from users. Another type of content that online systems would like to prohibit is deceptive content, for example, content including links that direct users to an improper external webpages or content whose main purpose is to attract attention and encourage visitors to click on the content item without providing significant information.
Online systems also user automatic techniques to detect content that violates policies of the online system. Such automatic detection is able to identify content items that violate simple policies, for example, presence of certain keywords indicating use of improper language. However these techniques are unable to identify violations of complex policies, for example, whether a content item includes deceptive content. Some online systems use human reviewers to identify content that violates such policies. However review of content by human reviewers is a slow and expensive process. Further human review process can be error prone. Therefore conventional techniques for identifying content violating policies of online systems have several drawbacks.